In this ITProPortal article, Zilliant Senior Vice President of Products & Science Pete Eppele shares the five biggest misconceptions about price optimization and the truths behind them. Read the full article here.
Technological disruptions tend to cause anxiety and elicit misconceptions. Early Americans called trains iron horses and found it dizzying to ride something that went as fast as 30 miles per hour. Telephones were said to deliver electric shocks to anyone who touched them. There are still claims that texting is ruining the English language.
It’s no wonder, then, that after the pandemic pushed digitization into overdrive, B2B pricing mythologies became more deeply ingrained into the collective psyche. Suddenly losing reliance on human interactions, many businesses had to upgrade their bare-bones websites into eCommerce sites capable of competing with Amazon. Longtime workflows, such as hand-edited pricing spreadsheets and monthly forecasting, suddenly appear woefully slow given the speed and scale of the internet. Businesses started to pay attention to pricing optimization platforms and automated data analytics — and the stories resurfaced.
Here are five of the biggest misconceptions about pricing optimization and the truths behind them.
Selling without a sales rep? You might as well declare that the world is flat. Conventional wisdom is that pricing is an art, selling is an art, and that sales reps who connect customers with products are the ultimate arbiters, and as such, should have the autonomy to negotiate prices.
Sales reps insist, “I know my customer, my market and my product better than anyone.” They develop trusted relationships and understand the intangibles. But data still counts. While a salesperson has one data point, a good pricing optimization platform has a world of data.
When rolling out new pricing software at sales events, we like to ask a room of salespeople to estimate the cost of a 500 pack of styrofoam containers. They text their estimates, and the numbers are projected onto a large screen. The numbers are all over the place and biased by the reps’ experience and customers (e.g., high-end or low-end restaurants). It’s an eye-opening exercise that demonstrates how, when pricing relies on gut feeling and intuition, the outcomes can vary widely. This allows us to demonstrate that with pricing, AI can think like your best reps think — carefully considering all the moving factors that influence price for each unique situation.
Read the full article here.